Diverse Geographical Region Analysis Based on Deforestation Rate Using Remote Sensing Image and Machine Learning Techniques

 

 

 

Abhilash S. Nath1,*, Manu Gupta2, J. Sirisha Devi3, A Babisha4, D. Venkata Ravi Kumar5,
B. Rama Subba Reddy6

 

1Assistant Professor, Department of Artificial Intelligence and Data Science, St. Joseph's Institute of Technology, OMR, Chennai, India

 

2Associate Professor, Department of Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, India

 

3Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad 500043, Telangana, India

 

4Assistant Professor, Department of Artificial intelligence and Data Science, Panimalar Engineering College, Chennai, Tamilnadu, India

 

5Assistant Professor, Department of Computer Science and Engineering, Aditya University, Surampalem

 

6Professor, Department of CSE, Mohan Babu University, Tirupati, India

 

Text Box: Abstract With direct implications for the regional climate, biogeochemistry, hydrology, and biodiversity, land cover change has been identified as one of the top priorities for the development of sustainable management plans. Among the primary causes of global warming are deforestation and forest fragmentation, which have profound effects on biodiversity preservation and ecosystem functioning. Machine learning techniques, like those employed in computer vision, have become widely used, making it possible to segment satellite images semantically to distinguish between areas that are forested and those that are not. This study presents a novel method for segmenting and classifying UAV images to detect deforestation using machine-learning models. In this case, noise reduction as well as normalisation is applied to input, which consists of UAV-based forest region photos. Semantic U-convolutional regressive neural network combined with deep radial quantile temporal neural network was then used to segment and classify this image. The suggested model's simulation analysis is assessed based on several metrics, including F-1 score, normalized coefficient ratio, average precision, AUC, and detection accuracy. proposed method yielded 97% detection accuracy, 93% normalized coefficient ratio, 91% AUC, F-1 score of 94% and 95% AVERAGE PRECISION. Emails: abhilashsathyanath2@gmail.com; Manugupta5416@gmail.com; Siri.cse21@gmail.com; babisha15@gmail.com; ravikumar.venkat@gmail.com; rsreddyphd@gmail.com

 

Received: February 07, 2025 Revised: May 25, 2025 Accepted: July 01, 2025

 

Keywords: Biodiversity; Deforestation rate; Machine learning model; Regressive neural network; Radial quantile temporal